During the past decades a considerable body of knowledge has been developed in the study of technological change, and much of it is being used by policy makers in the public and private spheres. While it is true to say that we now understand a great deal more about...
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This paper centres around two research questions: first, the identification of five types of networks that manufacturing firms located in the metropolitan region of Vienna may have created for different purposes and second, the question to what extent the likelihood of interfirm cooperation is conditioned by the general profile of manufacturing establishments and their technological resources. Although this paper focuses on the manufacturing sector, a special emphasis is placed on the electronics industry. The study utilises a recent postal survey providing data on size and organisation, products and markets, research and development, innovation and interfirm relationships. The analysis of the first question finds that first, networking does not yet seem to be a popular managerial and organisational concept for manufacturing firms located in the metropolitan region of Vienna; second, networking activities are primarily based on vertical relationships (customer, manufacturer supplier and producer service provider networks) rather than on horizontal linkages (producer networks, industry-university linkages); third, networks focusing on the later stages of the innovation process are less common than those focusing on the earlier stages; fourth, firms tend to rely on sources of technology from national and - especially - international networks. It appears that metropolitan networking is less common than has been thought. For technical advance spatial proximity does not seem to be very important. Turning to the second research question of the study, focusing on the adoption of the managerial and organisational concept of networking, the results are bolstering the argument that establishment traits and technology related-capabilities do play a role. The results achieved reveal, for example, that in-house research skills are a very good predictor for industry-university relationships.
No abstract is provided for this article.
This paper concentrates on the central link between productivity and knowledge capital, and shifts attention from firms and industries to regions. The objective is to measure knowledge elasticity effects within a regional Cobb-Douglas production function framework, with an emphasis on knowledge spillovers. The analysis uses a panel of 203 European regions to estimate the effects over the period 1997-2002. The dependent variable is total factor productivity (TFP). We use a region-level relative TFP index as an approximation to the true TFP measure. This index describes how efficiently each region transforms physical capital and labour into outputs. The explanatory variables are internal and out-of-region stocks of knowledge, the latter capturing the contribution of interregional knowledge spillovers. We use patents to measure knowledge capital. Patent stocks are constructed such that patents applied at the European Patent Office in one year add to the stock in the following and then depreciate throughout the patents effective life according to a rate of knowledge obsolescence. A random effects panel data spatial error model is advocated and implemented for analyzing the productivity effects. The findings provide a fairly remarkable confirmation of the role of knowledge capital contributing to productivity differences among regions, and adding an important dimension to the discussion, showing that knowledge spillover effects increase with geographic proximity.
Building a feedforward computational neural network model (CNN) involves two distinct tasks: determination of the network topology and weight estimation. The specification of a problem adequate network topology is a key issue and the primary focus of this contribution. Up to now, this issue has been either completely neglected in spatial application domains, or tackled by search heuristics (see Fischer and Gopal 1994). With the view of modelling interactions over geographic space, this paper considers this problem as a global optimization problem and proposes a novel approach that embeds backpropagation learning into the evolutionary paradigm of genetic algorithms. This is accomplished by interweaving a genetic search for finding an optimal CNN topology with gradient-based backpropagation learning for determining the network parameters. Thus, the model builder will be relieved of the burden of identifying appropriate CNN-topologies that will allow a problem to be solved with simple, but powerful learning mechanisms, such as backpropagation of gradient descent errors. The approach has been applied to the family of three inputs, single hidden layer, single output feedforward CNN models using interregional telecommunication traffic data for Austria, to illustrate its performance and to evaluate its robustness.
The transaxle construction is actually the designation for a transmission model where the vehicle transmission, the differential transmission, and the axle
Geographical Information Systems (GIS) are capable of acquiring spatially indexed data from a variety of sources, changing the data into useful formats, storing the data, retrieving and manupulating the data for analysis, and then generating the output required by a given user. Their great strength is based on the ability to handle large, multilayered, heterogenous databases and to query about the existence, location and properties of a wide range of spatial objects in an interactive way. The lack of analytical and modelling functionality is, however, widely recognised as a major deficiency of current systems. There is a wide agreement in both the GIS community and the modelling community that the future success of GIS technology will depend to a large extent on incorporating more powerful analytical and modelling capabilities. This paper discusses some major directions and strategies to increase both the analytical and modelling capabilities and the level of intelligence of geographic information systems.
No abstract is provided for this article.
This study suggests a two-step approach to identifying and interpreting regional convergence clubs in Europe. The first step involves identifying the number and composition of clubs using a space-time panel data model for annual income growth rates in conjunction with Bayesian model comparison methods. A second step uses a Bayesian space-time panel data model to assess how changes in the initial endowments of variables (that explain growth) impact regional income levels over time. These dynamic trajectories of changes in regional income levels over time allow us to draw inferences regarding the timing and magnitude of regional income responses to changes in the initial conditions for the clubs that have been identified in the first step. This is in contrast to conventional practice that involves setting the number of clubs ex ante, selecting the composition of the potential convergence clubs according to some a priori criterion (such as initial per capita income thresholds for example), and using cross-sectional growth regressions for estimation and interpretation purposes.
Although there is a substantial body of literature on labour market analysis, most of it ignores the spatial dimension of the labour market. A spatial perspective in analyzing labour market processes is important for several reasons (see Fischer, 1986). First, labour...
In the event of a severe accident with core melting in a NPP the stabilization of the molten corium is an important mitigation issue, as it can avoid late containment failure caused by basemat pene ...
In the event of a severe accident in a nuclear power plant with the core melting, the stabilization of the molten corium is an important mitigation issue, as it can avoid late containment failure caused by basemat penetration, overpressure, or severe damage to internal structures. The related failure modes may result in significant long-term radiological consequences and related high costs.Because of this, the licensing frameworks of several countries now include a requirement to implement mitigative core melt stabilization measures. This applies not only to new builds but also to existing light water reactors.The paper gives an overview of the ex-vessel core melt stabilization strategies developed during the last decades. These strategies are based on a variety of physical principles, like melt fragmentation in a deep water pool or during the molten core–concrete interaction with top flooding, water injection from the bottom (COMET), and retention in an outside-cooled crucible structure.This overview covers the physical background and functional principles of these concepts, as well as their validation status and, if applicable, the remaining open issues and research and development needs. For the concepts based on melt retention inside a cooled crucible that have reached sufficient maturity to be implemented in current Generation III+ designs, like the VVER-1000/1200 and the European Pressurized Water Reactor, more detailed descriptions are provided, which include key aspects of the related technical realization.The paper is compiled using contributions from the main developers of the individual concepts.
